book-movie-align.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Align the Book and Movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.io as sio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import pylcs\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the path for the scene break file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "SCENE_BREAK_PATH = \"processed/hp/scene_detect_output.mat\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Utilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def savecache(data, name):\n",
    "    np.save(f\"{name}.npy\", data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loadcache(name):\n",
    "    out = np.load(f\"{name}.npy\", allow_pickle=True)\n",
    "    if isinstance(out, np.ndarray):\n",
    "        return out\n",
    "    else:\n",
    "        return out.item()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Scene Breaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scene_breaks = sio.loadmat(SCENE_BREAK_PATH)['scene_breaks']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scene_breaks = scene_breaks - 1 # Convert from matlab indexing to python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scene_breaks = np.array(list(filter(lambda x: x[2], scene_breaks)))\n",
    "# scene_breaks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Shot Boundaries and TimeStamps"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the path to vid events computed earlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "VIDEVENTS_PATH = \"processed/hp/movie.videvents\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "shot_boundaries = pd.read_csv(VIDEVENTS_PATH, sep=' ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convert Scene Break to Time Maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_times = []\n",
    "end_times = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for scene_break in scene_breaks:\n",
    "    start_shot, end_shot, nshots = scene_break\n",
    "    \n",
    "    start_time, end_time = shot_boundaries.iloc[start_shot]['time'], shot_boundaries.iloc[end_shot-1]['time']\n",
    "    \n",
    "    start_times.append(start_time)\n",
    "    end_times.append(end_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scene_break_times = np.hstack([scene_breaks, np.array(start_times).reshape(-1, 1), np.array(end_times).reshape(-1, 1)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save to cache: start, end, #scenes, start_time, end_time "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "savecache(scene_break_times, \"scene_break_times\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get All Subtitles Within Range"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the subtitles and transcript path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "SRT_PATH = \"../Soundtracking-Books/data/HP/new_subtitles.srt\"\n",
    "TRANSCRIPT_JSON_PATH = \"../Soundtracking-Books/data/HP/new_transcript.json\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pysrt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pysrt.srttime import SubRipTime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ALL_SUBS = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "SUBTITLE = pysrt.open(SRT_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open(TRANSCRIPT_JSON_PATH, \"r\")\n",
    "f = json.load(f)\n",
    "TRANSCRIPT = f['transcript']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a video instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scripts.videoDialogueExtractor import Video"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "video = Video(TRANSCRIPT, scene_break_times)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subtitles within range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_subtitle_characters(start, end):\n",
    "    matched_subs = []\n",
    "    for subtitle in TRANSCRIPT:\n",
    "        \n",
    "        if subtitle['type']:\n",
    "            continue\n",
    "        \n",
    "        time = subtitle['time']\n",
    "        time_s = SubRipTime.from_string(subtitle['time'].split(\"-->\")[0])\n",
    "        time_ms = time_s.ordinal // 1000\n",
    "\n",
    "        if time_ms >= start and time_ms <= end:\n",
    "            if 'actor' in subtitle.keys():\n",
    "                matched_subs.append(subtitle['actor'])\n",
    "    return matched_subs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_subtitles(start, end):\n",
    "    matched_subs = []\n",
    "    for subtitle in TRANSCRIPT:\n",
    "        \n",
    "        if subtitle['type']:\n",
    "            continue\n",
    "\n",
    "        time = subtitle['time']\n",
    "        time_s = SubRipTime.from_string(subtitle['time'].split(\"-->\")[0])\n",
    "        time_ms = time_s.ordinal // 1000\n",
    "        \n",
    "#         time_ms = time_s.hour * 60 * 60 + time_s.minute * 60 + time_s.second\n",
    "\n",
    "        if time_ms >= start and time_ms <= end:\n",
    "            matched_subs.append(subtitle['line'])\n",
    "    return matched_subs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Histogram for Movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_movie_chars = []\n",
    "OTHER_INDEX = -100\n",
    "\n",
    "for scene in scene_break_times:\n",
    "    start, end, num, start_time, end_time = scene\n",
    "    subs = get_subtitle_characters(start_time, end_time)\n",
    "    \n",
    "    for sub_char in subs:\n",
    "        all_movie_chars.append(sub_char.lower())\n",
    "    \n",
    "all_movie_chars = list(set(all_movie_chars))\n",
    "charIndMapping = {char:i for i, char in enumerate(all_movie_chars)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'snape': 0,\n",
       " 'mrs. weasley': 1,\n",
       " 'flitwick': 2,\n",
       " 'fred': 3,\n",
       " 'hermione': 4,\n",
       " 'the fat lady': 5,\n",
       " 'neville longbottom': 6,\n",
       " 'hagrid': 7,\n",
       " 'petunia': 8,\n",
       " 'boy': 9,\n",
       " 'voldemort': 10,\n",
       " 'harry': 11,\n",
       " 'seamus': 12,\n",
       " 'gryffindors': 13,\n",
       " 'goblin': 14,\n",
       " 'griphook': 15,\n",
       " 'sorting hat': 16,\n",
       " 'oliver': 17,\n",
       " 'ron and harry': 18,\n",
       " 'unseen inhuman voice': 19,\n",
       " 'ron and seamus': 20,\n",
       " 'girl': 21,\n",
       " 'filch': 22,\n",
       " 'tom': 23,\n",
       " 'dudley': 24,\n",
       " 'fred and george': 25,\n",
       " 'hooch': 26,\n",
       " 'class': 27,\n",
       " 'ron': 28,\n",
       " 'neville': 29,\n",
       " 'percy': 30,\n",
       " 'vernon': 31,\n",
       " 'nick': 32,\n",
       " 'man in a painting': 33,\n",
       " 'bloody baron': 34,\n",
       " 'all': 35,\n",
       " 'crowd': 36,\n",
       " 'guard': 37,\n",
       " 'boy 2': 38,\n",
       " 'snake': 39,\n",
       " 'george': 40,\n",
       " 'flint': 41,\n",
       " 'mcgonagall': 42,\n",
       " 'lee': 43,\n",
       " 'firenze': 44,\n",
       " 'witch': 45,\n",
       " 'ginny': 46,\n",
       " 'dean': 47,\n",
       " 'ollivander': 48,\n",
       " 'dumbledore': 49,\n",
       " 'draco': 50,\n",
       " 'man': 51,\n",
       " 'all (including flitwick)': 52,\n",
       " 'quirrell': 53,\n",
       " 'students': 54,\n",
       " 'woman': 55}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Characters present:\\n\", charIndMapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "OTHER_INDEX = len(all_movie_chars)\n",
    "\n",
    "# Uncomment this if you want to include unknown characters\n",
    "# charIndMapping['other'] = OTHER_INDEX\n",
    "# all_movie_chars.append('other')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make default histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scene_char_histograms = []\n",
    "\n",
    "for scene in scene_break_times:\n",
    "    start, end, num, start_time, end_time = scene\n",
    "    subs = get_subtitle_characters(start_time, end_time)    \n",
    "    \n",
    "    scene_hist = list(np.zeros(len(all_movie_chars)))\n",
    "    \n",
    "    for sub_char in subs:\n",
    "        scene_hist[charIndMapping[sub_char.lower()]] += 1\n",
    "    \n",
    "    scene_char_histograms.append(scene_hist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get All Book Names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scripts.characterNameBookParser import getSpeakerChapterNames, getMentionedChapterNames"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set path to the processed book (from `jsonify_book`) and the book text files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "BOOK_PROC_PATH = \"../book-process/processed_data/Harry_Potter\"\n",
    "BOOK_TEXT_PATH = \"../Soundtracking-Books/data/HP/chapters/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chapter_names = os.listdir(BOOK_PROC_PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get Character Occurence per chapter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chapterCharacterCount = list(map(lambda x: getSpeakerChapterNames(os.path.join(BOOK_PROC_PATH, x, x)), chapter_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make character list after taking union"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "allBookNames = []\n",
    "\n",
    "for chapCharCount in chapterCharacterCount:\n",
    "    for key in chapCharCount.keys():\n",
    "        allBookNames.append(key.lower())\n",
    "        \n",
    "allBookNames = list(set(allBookNames))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Match names in book to subtitle names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "draco malfoy - draco\n",
      "snape - snape\n",
      "grunt - man in a painting\n",
      "mrs. weasley - mrs. weasley\n",
      "oliver wood - oliver\n",
      "fred - fred\n",
      "hermione - hermione\n",
      "piers polkiss - man in a painting\n",
      "hagrid - hagrid\n",
      "fred weasley - mrs. weasley\n",
      "petunia - petunia\n",
      "gryffindor keeper wood - man in a painting\n",
      "dudley dursley - dudley\n",
      "nicholas flamel dumbledore - dumbledore\n",
      "harry - harry\n",
      "ronan - ron and harry\n",
      "diagon alley harry - ron and harry\n",
      "griphook - griphook\n",
      "mrs. dursley mapped to other\n",
      "wood mapped to other\n",
      "the gryffindors - gryffindors\n",
      "professor quirrell - quirrell\n",
      "mrs. norris mapped to other\n",
      "sir nicholas de mimsy mapped to other\n",
      "filch - filch\n",
      "dudley - dudley\n",
      "professor flitwick - flitwick\n",
      "rubeus hagrid - hagrid\n",
      "ron - ron and harry\n",
      "malfoy - man in a painting\n",
      "neville - neville longbottom\n",
      "professor dumbledore - dumbledore\n",
      "percy - percy\n",
      "madam malkin - man in a painting\n",
      "gryffindor - man in a painting\n",
      "hufflepuff mapped to other\n",
      "diggle mapped to other\n",
      "friar mapped to other\n",
      "madam pomfrey - fred and george\n",
      "halloween malfoy - all\n",
      "hermione granger - hermione\n",
      "argus filch - filch\n",
      "mr. ollivander - ollivander\n",
      "nearly headless nick - nick\n",
      "chaser angelina johnson - ron and harry\n",
      "uncle vernon - vernon\n",
      "the weasleys - mrs. weasley\n",
      "finnigan - man in a painting\n",
      "professor snape - snape\n",
      "seamus finnigan - seamus\n",
      "slytherins - the fat lady\n",
      "parvati patil - man in a painting\n",
      "george - fred and george\n",
      "miss granger - man in a painting\n",
      "pansy parkinson - man in a painting\n",
      "slytherin - the fat lady\n",
      "firenze - firenze\n",
      "aunt petunia - petunia\n",
      "goyle mapped to other\n",
      "percy weasley - mrs. weasley\n",
      "george weasley - mrs. weasley\n",
      "lord voldemort - voldemort\n",
      "mr. dursley mapped to other\n",
      "harry potter - harry\n",
      "madam hooch - hooch\n",
      "norbert the norwegian ridgeback quirrell - quirrell\n",
      "albus dumbledore - dumbledore\n",
      "dean - dean\n",
      "bane - man in a painting\n",
      "professor mcgonagall - mcgonagall\n",
      "dumbledore - dumbledore\n",
      "ginny weasley - mrs. weasley\n",
      "ravenclaw - all (including flitwick)\n",
      "gloom mapped to other\n",
      "the dursleys - the fat lady\n",
      "jordan - man in a painting\n",
      "peeves mapped to other\n",
      "quirrell - quirrell\n",
      "susan - man in a painting\n"
     ]
    }
   ],
   "source": [
    "bookMovieNameMap = {}\n",
    "\n",
    "for name in allBookNames:\n",
    "    maxLCS = 0\n",
    "    newname = \"NONE\"\n",
    "    \n",
    "    filtered_name = name.replace(\"professor\", '').replace(\"aunt\", '').replace(\"uncle\", '').strip()\n",
    "    \n",
    "    for movie_name in all_movie_chars:        \n",
    "        lcs = pylcs.lcs2(movie_name, filtered_name)\n",
    "                \n",
    "        if lcs > maxLCS:\n",
    "            \n",
    "            words = list(filter(lambda x: len(x) > 1 and x.isalpha(), movie_name.split()))\n",
    "            word_length = list(map(lambda x: len(x), words))\n",
    "\n",
    "            if len(word_length) == 0:\n",
    "                continue\n",
    "            \n",
    "            if lcs not in word_length and not (lcs > np.max(word_length)):\n",
    "                continue\n",
    "            \n",
    "            maxLCS = lcs\n",
    "            newname = movie_name\n",
    "            \n",
    "    if newname == \"NONE\":\n",
    "        print(name, \"mapped to other\")\n",
    "        bookMovieNameMap[name] = 'other'\n",
    "    else:\n",
    "        print(name, \"-\", newname)\n",
    "        bookMovieNameMap[name] = newname      "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Manually fix the incorrect ones - change depending on your book"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bookMovieNameMap['ginny weasley'] = 'ginny'\n",
    "bookMovieNameMap['slytherins'] = 'other'\n",
    "bookMovieNameMap['madam malkin'] = 'other'\n",
    "bookMovieNameMap['professor flitwick'] = 'flitwick'\n",
    "bookMovieNameMap['madam pomfrey'] = 'other'\n",
    "bookMovieNameMap['bane'] = 'other'\n",
    "bookMovieNameMap['jordan'] = 'other'\n",
    "bookMovieNameMap['grunt'] = 'other'\n",
    "bookMovieNameMap['norbert the norwegian ridgeback quirrell'] = 'other'\n",
    "bookMovieNameMap['mrs. dursley'] = 'petunia'\n",
    "bookMovieNameMap['nicholas flamel dumbledore'] = 'other'\n",
    "bookMovieNameMap['harry potter'] = 'harry'\n",
    "bookMovieNameMap['george weasley'] = 'george'\n",
    "bookMovieNameMap['george'] = 'george'\n",
    "bookMovieNameMap['seamus finnigan'] = 'seamus'\n",
    "bookMovieNameMap['finnigan'] = 'other'\n",
    "bookMovieNameMap['harry '] = 'harry'\n",
    "bookMovieNameMap['malfoy'] = 'draco'\n",
    "bookMovieNameMap['gryffindor'] = 'other'\n",
    "bookMovieNameMap['miss granger'] = 'hermione'\n",
    "bookMovieNameMap['gryffindor keeper wood'] = 'other'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Book Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "book_chap_histograms = []\n",
    "\n",
    "for chapCharCount in chapterCharacterCount:\n",
    "\n",
    "    chap_hist = list(np.zeros(len(all_movie_chars)))\n",
    "\n",
    "    for key in chapCharCount.keys():\n",
    "        name = key.lower()\n",
    "        if bookMovieNameMap[name] != 'other':\n",
    "            chap_hist[charIndMapping[bookMovieNameMap[name]]] += chapCharCount[key]\n",
    "\n",
    "    book_chap_histograms.append(chap_hist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'snape': 0,\n",
       " 'mrs. weasley': 1,\n",
       " 'flitwick': 2,\n",
       " 'fred': 3,\n",
       " 'hermione': 4,\n",
       " 'the fat lady': 5,\n",
       " 'neville longbottom': 6,\n",
       " 'hagrid': 7,\n",
       " 'petunia': 8,\n",
       " 'boy': 9,\n",
       " 'voldemort': 10,\n",
       " 'harry': 11,\n",
       " 'seamus': 12,\n",
       " 'gryffindors': 13,\n",
       " 'goblin': 14,\n",
       " 'griphook': 15,\n",
       " 'sorting hat': 16,\n",
       " 'oliver': 17,\n",
       " 'ron and harry': 18,\n",
       " 'unseen inhuman voice': 19,\n",
       " 'ron and seamus': 20,\n",
       " 'girl': 21,\n",
       " 'filch': 22,\n",
       " 'tom': 23,\n",
       " 'dudley': 24,\n",
       " 'fred and george': 25,\n",
       " 'hooch': 26,\n",
       " 'class': 27,\n",
       " 'ron': 28,\n",
       " 'neville': 29,\n",
       " 'percy': 30,\n",
       " 'vernon': 31,\n",
       " 'nick': 32,\n",
       " 'man in a painting': 33,\n",
       " 'bloody baron': 34,\n",
       " 'all': 35,\n",
       " 'crowd': 36,\n",
       " 'guard': 37,\n",
       " 'boy 2': 38,\n",
       " 'snake': 39,\n",
       " 'george': 40,\n",
       " 'flint': 41,\n",
       " 'mcgonagall': 42,\n",
       " 'lee': 43,\n",
       " 'firenze': 44,\n",
       " 'witch': 45,\n",
       " 'ginny': 46,\n",
       " 'dean': 47,\n",
       " 'ollivander': 48,\n",
       " 'dumbledore': 49,\n",
       " 'draco': 50,\n",
       " 'man': 51,\n",
       " 'all (including flitwick)': 52,\n",
       " 'quirrell': 53,\n",
       " 'students': 54,\n",
       " 'woman': 55}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "charIndMapping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use inverse frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 51.,  27.,   7.,   8., 171.,   7.,  34., 301.,  29.,   0.,   3.,\n",
       "       469.,   4.,   1.,   0.,   4.,   0.,  23., 431.,   0.,   0.,   0.,\n",
       "        23.,   0.,  29.,   0.,  15.,   0.,   0.,   0.,   3.,  74.,   1.,\n",
       "         5.,   0.,   5.,   0.,   0.,   0.,   0.,  12.,   0.,  58.,   0.,\n",
       "        13.,   0.,   4.,   5.,  21.,  99.,  30.,   0.,   1.,  54.,   0.,\n",
       "         0.])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "book_chap_all = np.sum(book_chap_histograms, 0)\n",
    "# book_chap_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jd/anaconda3/envs/music/lib/python3.6/site-packages/ipykernel_launcher.py:1: RuntimeWarning: divide by zero encountered in log\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/home/jd/anaconda3/envs/music/lib/python3.6/site-packages/ipykernel_launcher.py:1: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "itf_character = 1/np.log(book_chap_all)\n",
    "itf_character[itf_character == np.inf] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.25433478,  0.30341308,  0.51389834,  0.48089835,  0.19448958,\n",
       "        0.51389834,  0.28357849,  0.17522002,  0.2969742 , -0.        ,\n",
       "        0.91023923,  0.16258569,  0.72134752,  0.        , -0.        ,\n",
       "        0.72134752, -0.        ,  0.31892899,  0.16485034, -0.        ,\n",
       "       -0.        , -0.        ,  0.31892899, -0.        ,  0.2969742 ,\n",
       "       -0.        ,  0.36926937, -0.        , -0.        , -0.        ,\n",
       "        0.91023923,  0.23233849,  0.        ,  0.62133493, -0.        ,\n",
       "        0.62133493, -0.        , -0.        , -0.        , -0.        ,\n",
       "        0.4024296 , -0.        ,  0.24627855, -0.        ,  0.38987125,\n",
       "       -0.        ,  0.72134752,  0.62133493,  0.32845874,  0.21762218,\n",
       "        0.2940141 , -0.        ,  0.        ,  0.2506904 , -0.        ,\n",
       "       -0.        ])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "itf_character"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compute Similarity Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from scipy import spatial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  5.,  5.,  0.,  0.,  0.,  0.,\n",
       "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "        0.,  0.,  0., 24.,  0.,  0.,  0.,  0.,  0.,  0., 31.,  0.,  0.,\n",
       "        0.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# np.array(book_chap_histograms[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.87610012,  1.48487102, -0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        , -0.        ,\n",
       "        0.        , -0.        ,  0.        ,  0.        , -0.        ,\n",
       "       -0.        , -0.        ,  0.        , -0.        ,  0.        ,\n",
       "       -0.        ,  0.        , -0.        , -0.        , -0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        , -0.        ,\n",
       "        0.        , -0.        , -0.        , -0.        , -0.        ,\n",
       "        0.        , -0.        ,  5.9106851 , -0.        ,  0.        ,\n",
       "       -0.        ,  0.        ,  0.        ,  0.        ,  6.74628759,\n",
       "        0.        , -0.        ,  0.        ,  0.        , -0.        ,\n",
       "       -0.        ])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# book_chap_histograms[0] * itf_character"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7faf1f1602e8>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(book_chap_histograms * itf_character)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7faf1f0356a0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(book_chap_histograms)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CHAP_COUNT = len(book_chap_histograms)\n",
    "SCENE_COUNT = len(scene_char_histograms)\n",
    "\n",
    "characterSimMat = np.zeros((CHAP_COUNT, SCENE_COUNT))\n",
    "\n",
    "norm_book_chap_histograms = normalize(book_chap_histograms, axis=1, norm='l2')\n",
    "norm_scene_char_histograms = normalize(scene_char_histograms, axis=1, norm='l2')\n",
    "\n",
    "for i in range(CHAP_COUNT):\n",
    "    for j in range(SCENE_COUNT):\n",
    "        \n",
    "        characterSimMat[i][j] = (2 ** 0.5) - np.linalg.norm(norm_scene_char_histograms[j] - norm_book_chap_histograms[i])\n",
    "#         characterSimMat[i][j] = 1 - spatial.distance.cosine(norm_book_chap_histograms[i], norm_scene_char_histograms[j])\n",
    "    \n",
    "        if np.isnan(characterSimMat[i][j]):\n",
    "            characterSimMat[i][j] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 7200x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(characterSimMat/np.max(characterSimMat.flatten()))\n",
    "plt.colorbar()\n",
    "plt.gcf().set_figwidth(100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "normCharacterSimMat = characterSimMat/np.max(characterSimMat.flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "savecache(characterSimMat, 'characterSimMat_120_axis0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "characterSimMat = loadcache('characterSimMat_120_axis0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Next Part: Dialogue Matching "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.tokenize import word_tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scripts.bookQuoteExtractor import Book, getChapterQuotes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chapters = sorted(os.listdir(BOOK_TEXT_PATH), key=lambda x: int(x.split('-')[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create book class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "book = Book(chapters, BOOK_TEXT_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chapterText = book.chapterContent\n",
    "chapterText = list(map(lambda x: x.getText(), chapterText))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get all subtitles / dialogues in movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sceneText = []\n",
    "\n",
    "for scene in scene_break_times:\n",
    "    start, end, num, start_time, end_time = scene\n",
    "    subs = get_subtitles(start_time, end_time)\n",
    "    \n",
    "    sceneText.append(subs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1269,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scripts.lcs import lcs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1270,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scripts.utils import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute Term Frequency for all words in video"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'remove_punctuation' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-190-6f43f009a157>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mvideoTextAll\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m' '\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'line'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubtitles\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mvideoTextWords\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mremove_punctuation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mremove_extra_whitespace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvideoTextAll\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mvideoWordCount\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCounter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvideoTextWords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-190-6f43f009a157>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mvideoTextAll\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m' '\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'line'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubtitles\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mvideoTextWords\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mremove_punctuation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mremove_extra_whitespace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvideoTextAll\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mvideoWordCount\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCounter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvideoTextWords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'remove_punctuation' is not defined"
     ]
    }
   ],
   "source": [
    "f = open(TRANSCRIPT_JSON_PATH, \"r\")\n",
    "subtitles = json.load(f)\n",
    "subtitles = subtitles['transcript']\n",
    "\n",
    "videoTextAll = ' '.join(list(map(lambda x: x['line'], subtitles))).lower()\n",
    "videoTextWords = list(map(lambda x: remove_punctuation(remove_extra_whitespace(x)), videoTextAll.split()))\n",
    "videoWordCount = dict(Counter(videoTextWords))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute term frequency for all words in book"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bookText = book.getAllText().lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bookWords = list(map(lambda x: remove_punctuation(remove_extra_whitespace(x)), bookText.split()))\n",
    "bookWordCount = dict(Counter(bookWords))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Go Through every chapter, get all dialogues in the chapter, and compute the score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1271,
   "metadata": {},
   "outputs": [],
   "source": [
    "chapterQuotes = list(map(lambda x: getChapterQuotes(os.path.join(BOOK_TEXT_PATH, x + \".txt\")), chapter_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1272,
   "metadata": {},
   "outputs": [],
   "source": [
    "videoDialogues = list(map(lambda x: video.get_subtitles_in_scene(x), np.arange(len(video))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1273,
   "metadata": {},
   "outputs": [],
   "source": [
    "def text_process_dialogue(sent):\n",
    "    sent = lower_text(sent)\n",
    "    sent = remove_extra_whitespace(sent)\n",
    "    sent = remove_punctuation(sent)\n",
    "    sent = word_tokenize(sent)\n",
    "    \n",
    "    return sent\n",
    "    \n",
    "def text_process_dialogues(sents):\n",
    "    return list(map(text_process_dialogue, sents))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1274,
   "metadata": {},
   "outputs": [],
   "source": [
    "chapterQuotes = list(map(lambda x: text_process_dialogues(x), chapterQuotes))\n",
    "videoDialogues = list(map(lambda x: text_process_dialogues(x), videoDialogues))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1275,
   "metadata": {},
   "outputs": [],
   "source": [
    "from multiprocessing import Pool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1284,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00  Chapter2 Chapter  2051 Chapter   SceneChapterScene 70 Done: Done: 3  Scene119  00  \n",
      "ChapterDone:\n",
      "SceneChapter--------   --------154 \n",
      "\n",
      "5.66642668811243235\n",
      "Done:  03Chapter  Scene--------  Scene 20.945815724076905Chapter\n",
      "86  Done:\n",
      " ChapterDone:  --------52  Scene11.90104093474328\n",
      "  351.36981634154611105\n",
      "SceneDone: \n",
      "2 -------- Scene -------- \n",
      " Chapter\n",
      "Done:Chapter93.8110029058255 Done:3  \n",
      " 32 071-------- \n",
      " Chapter 145.4316685127715Scene\n",
      "Chapter Scene\n",
      "--------  Done: --------\n",
      "5520  Done:\n",
      " 131.96945930351913Scene 19.36293747049243Scene 1\n",
      " 0Done: --------\n",
      "Done: Chapter \n",
      "Chapter--------  0 \n",
      "13.631972261242424361\n",
      "\n",
      " -------- 1--------SceneScene\n",
      "  \n",
      " 02ChapterDone:Done:   87  Chapter61.784885534746586493.7964153945328 ChapterScene \n",
      "\n",
      "  72Done:1020----------------   \n",
      "\n",
      "61.252449081784725 SceneScene\n",
      "  Chapter--------Done:Done:\n",
      "   23282.7667268376815539.903087472514787  1\n",
      "  \n",
      "ChapterChapter--------ChapterScene-------- \n",
      "   5621\n",
      "37Done:    SceneSceneScene49.72998250064301  1 \n",
      "Done:Done: --------Done:  Chapter\n",
      " 121.9818286203590897.24401123140595 2\n",
      "82.11190433358819\n",
      "\n",
      "------------------------ 88Chapter \n",
      "\n",
      "\n",
      " Scene 3Done:730  Chapter Scene   Chapter33.6893243931092957Done: \n",
      " 54 --------62.604018052042775 \n",
      "SceneScene\n",
      "2 --------\n",
      " 0 Done:Done:Chapter 2  Chapter  17.86125293834715711.379165474790705 Chapter22\n",
      "\n",
      "2  ---------------- 74Scene\n",
      "\n",
      "0 Scene  0Scene Done:Chapter  Done: Chapter  Done:82.13691383129172103  269.9571949303431\n",
      " 550--------\n",
      "\n",
      "--------\n",
      " Scene--------\n",
      "3Scene 1\n",
      "  Done: Done:Chapter3 Chapter 0 284.1425917206805  58Chapter\n",
      "89\n",
      "  -------- Scene--------4\n",
      "Scene \n",
      "  Done:SceneDone: 3  5.5451774444795621 Done:86.06745968172149\n",
      "Chapter   \n",
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      "106 Chapter\n",
      " \n",
      "38--------Scene  0\n",
      "Scene23    Done:SceneDone: Chapter0  327.39758215239976  244.8441221226234Done:\n",
      "Chapter0-------- \n",
      "56  \n",
      "--------52.83038307521042 Chapter104\n",
      "\n",
      " Scene 2--------31 Scene 1\n",
      "   ChapterDone: Done:SceneChapter  Chapter   130.9257131640435775108.85773901984558Done: \n",
      "90\n",
      " 39  ---------------- 194.97025631555235Scene\n",
      "Scene\n",
      "Scene\n",
      "   --------Done:Done:Done:\n",
      "  221.552085469729 0\n",
      "094.66859728341169 \n",
      "\n",
      "3--------2\n",
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      "Chapter2\n",
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      "Done:  42.25682122429074Done:\n",
      "--------ChapterChapter \n",
      "   \n",
      "----------------0105400\n",
      "\n",
      "\n",
      "  \n",
      "--------Scene--------Scene\n",
      " 0 \n",
      "Done: Done: Chapter 140.83534320351285 22.882532846704972\n",
      "\n",
      "58---------------- \n",
      "2\n",
      "Scene Chapter  77Done:  Scene2.197224577336219612 \n",
      " Done:--------  Chapter38.42772248362448\n",
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      " Done:Done:Done:Scene 85.9498270180512   59 \n",
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      " \n",
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      "Scene--------2----------------\n",
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      "\n",
      " Done: \n",
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      "\n",
      "   ----------------SceneScene\n",
      "Done:\n",
      "   94.89864903106938Done:Done: 0 \n",
      " 27.10542777494148235.448927568206884--------\n",
      "\n",
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      " --------106\n",
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      "   27 Scene--------62Done:Scene  Scene0\n",
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      " 6.556778356158042Done: Done:  \n",
      "--------47.5378610650757956079.85297599161906214.6504990283988 --------\n",
      "\n",
      "\n",
      " \n",
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      "--------\n",
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      " --------\n",
      "\n",
      "Done:\n",
      " 3136.80723444803752 1\n",
      "Chapter --------2 108\n",
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      " Scene   --------38.42402006684626Done:77.92201804095623 Done:\n",
      "Done:\n",
      " \n",
      " Done:3-------- 0 56.896114677099085-------- \n",
      "\n",
      "\n",
      "63.727707739047470--------\n",
      "59.98883940307067Chapter2-------- \n",
      "\n",
      "\n",
      "  \n",
      "--------Chapter--------\n",
      "63 Chapter\n",
      " 13 5Scene   82 ChapterChapterScene  Done:  Scene96 109Done:  55.60173135333815 Done: \n",
      "SceneScene467.53721010342207  -------- \n",
      "0\n",
      "Done:Done:--------\n",
      "3 \n",
      " 19.34994692934891--------8.196987927258895 \n",
      "Chapter\n",
      "\n",
      " 1--------8-------- \n",
      " \n",
      "3ChapterScene0    Chapter45ChapterDone:    110Scene6251.200859866505525  \n",
      " Scene--------SceneDone: 3 \n",
      " Done:Done: 394.26836135638091 Chapter2\n",
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      "ChapterChapter0 ----------------9  Scene \n",
      "\n",
      "29 83 Chapter3Scene Done:    Scene1Scene 107ChapterDone:  85.5282310921044  1  Chapter\n",
      "Done:SceneDone:  0111 --------  46\n",
      "Chapter 198.85073826421456\n",
      "Done:187.46378483454816 --------Scene \n",
      "3 \n",
      " Scene\n",
      "--------970569.7633489301368-------- Done: \n",
      " \n",
      " Chapter\n",
      " Done:--------Scene  Chapter02\n",
      " 65\n",
      "Done: 17.253584657768588  6--------Chapter \n",
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      "   Done: --------ChapterScene ChapterDone: \n",
      "0 \n",
      "Done:  10221.84743290641842 63 --------\n",
      "Scene\n",
      " --------0 Scene\n",
      "\n",
      " --------Done:1Done:\n",
      "  44.37295462157537421 101.46104239018258\n",
      "  Chapter\n",
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      "\n",
      "\n",
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      " \n",
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      "Done:0084 \n",
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      "0 \n",
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      "Done:  0--------\n",
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      "\n",
      "0Done:Done:    -------- Chapter8Chapter62.97756397554809456.76763944808074  \n",
      "\n",
      " \n",
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      "\n",
      "Scene0Done:Scene3  2   Chapter Done:Done:95.01366406307898 Chapter Chapter 26.16824937969142\n",
      " 0109 \n",
      "--------66 \n",
      "851----------------\n",
      " Scene  \n",
      "\n",
      "Scene 0SceneChapter Done:   Done:Chapter 12Done:49     14.443671650576897 ChapterChapter337.9415842371068442.5830581263001459\n",
      "Scene  \n",
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      "  --------10033\n",
      "----------------SceneDone:  0\n",
      "Scene  \n",
      "Scene 19.96491601076704Done:  \n",
      " Done:Done:Chapter 0--------  \n",
      "131.00318338153346\n",
      "105.54313312303346110\n",
      "--------\n",
      " --------\n",
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      " 1Done:0   ChapterChapter031 \n",
      "   101--------ChapterChapter210 \n",
      "   67Scene0 50 Chapter   Scene SceneDone:Chapter3Scene 86     Done: Done:11145.28221428929109ChapterDone:Scene   \n",
      " 53.8099718841101  92.78160229752615--------Scene13Done:65.20614624942935\n",
      "\n",
      " \n",
      "  \n",
      "--------2--------Done:108.71210859412092Scene\n",
      "--------\n",
      "   \n",
      "\n",
      "0ChapterDone:--------0 1 \n",
      "Chapter  \n",
      "34191.482657573211Chapter--------0 \n",
      "   \n",
      "11--------Scene\n",
      "51 3Chapter  Scene  Done:Scene66Chapter   1   Done:65.47568508636189Done:\n",
      "Scene113Chapter   -------- 0 0Done:102\n",
      " \n",
      "Scene\n",
      "-------- 2430.142243111004 --------\n",
      "Scene \n",
      "ChapterDone:\n",
      " -------- Done:\n",
      "1 355.877672018491187  \n",
      "67.4533125412053 Chapter--------\n",
      "Scene \n",
      "--------\n",
      "3 52 2Done: 0Chapter  Scene90.85352962116046  Chapter \n",
      "Done:Chapter68 --------35   3\n",
      "Scene12 0 \n",
      "  --------ChapterSceneDone:Scene1\n",
      "  0   114 Done:5.398162701517753ChapterDone:Chapter\n",
      " 0    Scene 86.1903975724202210.333905401999868671--------3Chapter \n",
      " 103\n",
      "  \n",
      " Done:--------Chapter Scene--------Chapter112 \n",
      "  2Scene\n",
      "80.6030088095984  Done: 53 \n",
      "14Scene ChapterDone:  -------- 142.23591578458687Scene  Done:Scene\n",
      "\n",
      "88 67.66641284966367 -------- Done: Scene162.8503698656121Done: \n",
      "\n",
      " 7.052721049232323--------\n",
      " 473.31122834560114\n",
      "--------\n",
      "\n",
      "--------\n",
      "Done:--------\n",
      "1 3\n",
      " 2 1Chapter29.357970916150663   ChapterChapter 104Chapter\n",
      "36 --------  54 Scene\n",
      "69 Scene  Scene Done: SceneDone: Done:  43.7981805681975878.66867903919486 Done:\n",
      "\n",
      "5.442417710521794 --------0--------27.46773313784753\n",
      "\n",
      " 0\n",
      "\n",
      "--------Chapter-------- \n",
      " \n",
      "Chapter168 1 13  Chapter2 ChapterScene   ChapterScene55 Done: 3  10537  Done:2SceneChapter 8.938006576471201     Scene\n",
      "Scene309.76913617499736Done:70Chapter -------- \n",
      "  Done: \n",
      "0Done:--------Scene89 \n",
      "\n",
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      "13 Chapter 46 Scene Done: 46.51297830396596615\n",
      " Chapter-------- 16\n",
      "141494    ChapterChapterChapterScene    11311524Done:    SceneSceneScene504.9757949315908   \n",
      "Done:Done:Done:--------   \n",
      "161097.4237069356368485.1935791592508379.3214360003574 \n",
      "\n",
      "Chapter\n",
      "------------------------ \n",
      "\n",
      "79\n",
      " Scene Done: 282.9379148041685\n",
      "--------\n",
      "16 Chapter 114 Scene Done: 56.448808893856146\n",
      "14--------\n",
      " Chapter 75 Scene Done:16  Chapter1023.2623583851865 \n",
      "--------80\n",
      " Scene16 Chapter 115 Scene Done: 660.9515654443486\n",
      "--------\n",
      "16 Chapter 25 14Scene  Chapter Done:76  680.0069760258904Scene\n",
      " --------Done:\n",
      " 0\n",
      "15 --------Done: \n",
      "15 Chapter 138.26302022419978 14Chapter\n",
      "47  --------14 Chapter95\n",
      "  Scene Chapter116Scene   Done: 1416 Chapter 81 SceneScene  Done:Done:  368.10979995606414386.0040748558946\n",
      "--------\n",
      "\n",
      "--------16\n",
      " Chapter 82 Scene Done: 0 783.8623606481677\n",
      "--------\n",
      "\n",
      "--------\n",
      "16 Chapter Done: 947.7598414036021\n",
      "-------- Chapter 77 Scene Done: \n",
      "266.92218927938256\n",
      "--------\n",
      "14 Chapter 1171626   SceneChapterScene   Done:83Done:   271.6479660903145Scene671.4231680706908 \n",
      "\n",
      "----------------Done:\n",
      " \n",
      "1059.382111801340415 \n",
      "Chapter-------- \n",
      "0 Scene Done: 0\n",
      "--------\n",
      "1516 15 Chapter 25Chapter Chapter  48 Scene116   96SceneSceneDone:   Scene Done:Done:676.8532676399169   Done:\n",
      "908.6170873486262603.0346751834677 --------\n",
      "\n",
      "\n",
      "285.5037611199175----------------\n",
      "\n",
      "\n",
      "--------\n",
      "14 Chapter 78 Scene Done:15 Chapter 1 Scene Done: 562.1492679667222\n",
      " 443.1154313983157--------\n",
      "\n",
      "--------\n",
      "15 Chapter 49 Scene gon15Done:   Chaptermissing164.85195527569817 \n",
      "\n",
      "97na--------  \n",
      "missingScene\n",
      " Done:16  gon1289.988890874056616 Chapter \n",
      " missingChapter16\n",
      "117--------   na\n",
      "27SceneChapter   missing Done:Scene\n",
      "84   560.5387062123443Done:Scene\n",
      " gon --------419.1222848690833 \n",
      "Done:\n",
      "missing --------884.058190808449\n",
      "\n",
      "\n",
      "na --------missing\n",
      "\n",
      "14 Chapter 79 Scene Done: 273.69563909120166\n",
      "--------\n",
      "15 Chapter 15 50 ChapterScene  98Done: Scene  892.5733055425725\n",
      "Done:-------- \n",
      "358.9429596306358\n",
      "15-------- Chapter\n",
      " 5114  SceneChapter  80Done:  0Scene\n",
      " Done:-------- \n",
      "16154.22983842329072 \n",
      "15Chapter--------  \n",
      "Chapter28  141552Scene    ChapterChapterSceneDone:    262Done: 338.3218569065274416  Scene\n",
      " 32.16583895094213Scene --------Chapter \n",
      "Done:\n",
      "Done:--------   340.2694269272743\n",
      "--------\n",
      "16 Chapter 2915  SceneChapter  99Done:  1054.3405678027846Scene\n",
      " Done:--------\n",
      " 14337.8342083110477 \n",
      "346.3370662044998\n",
      "Chapter-------- --------\n",
      "81\n",
      "16  SceneChapter Done:  30303.0939177751902 \n",
      "Scene --------Done:\n",
      " 0\n",
      "\n",
      "--------\n",
      "1685  ChapterScene  31Done:  Scene403.23182504392554 \n",
      "Done:-------- \n",
      "0\n",
      "--------\n",
      "15 Chapter 3 Scene Done: 248.147716958601\n",
      "--------\n",
      "15 Chapter 53 Scene Done: 103.57134108168448\n",
      "--------\n",
      "15 Chapter 54 Scene Done: 56.51652205173369\n",
      "--------\n",
      "15 Chapter 55 Scene Done: 0\n",
      "--------\n",
      "14 Chapter 27 Scene Done: 489.1821873920926\n",
      "--------\n",
      "15 Chapter16  56 ChapterScene  32Done:  Scene15226.43096003457023  Done:\n",
      "Chapter -------- \n",
      "100406.23283421613434\n",
      " --------Scene\n",
      " Done: 1180.6236364405993\n",
      "--------\n",
      "15 Chapter 57 Scene Done: 74.10868552544599\n",
      "--------\n",
      "15 Chapter 101 Scene Done: 220.8207036038259\n",
      "--------\n",
      "15 Chapter 58 Scene Done: 53.83272790129032\n",
      "15-------- Chapter\n",
      " 4 Scene Done: 614.393793932999414\n",
      " --------Chapter\n",
      " 28 Scene16  Done: Chapter316.1055229894931 \n",
      "86-------- \n",
      "Scene Done: 840.1509340431126\n",
      "--------\n",
      "16 15 ChapterChapter  3359  Scene SceneDone:  Done:319.15639568846314 \n",
      "164.17910532298353--------\n",
      "\n",
      "--------\n",
      "15 Chapter 102 Scene Done: 993.4508436685932\n",
      "--------\n",
      "15 Chapter 60 Scene Done: 318.0625881251698\n",
      "--------\n",
      "15 16Chapter Chapter  6187  SceneScene  Done:Done:  345.05604382351460\n",
      "\n",
      "----------------\n",
      "\n",
      "15 15 ChapterChapter 5 103 15  Scene SceneChapterDone:   Done:621130.2756030084  \n",
      "724.214265014625--------Scene\n",
      "\n",
      " --------Done:\n",
      " 206.0285706897575\n",
      "--------\n",
      "16 Chapter 34 14Scene  ChapterDone:  362.7971733503707729\n",
      " Scene-------- \n",
      "Done: 1039.4844269818202\n",
      "--------\n",
      "14 Chapter 30 Scene Done: 0\n",
      "--------\n",
      "15 Chapter 104 Scene Done: 283.3923084693122\n",
      "--------\n",
      "16 Chapter 1588  ChapterScene  63 Done: Scene 220.18615545112013\n",
      "Done: --------\n",
      "179.5036138772654\n",
      "--------\n",
      "15 Chapter 615  SceneChapter  Done:105  380.3065678569368Scene\n",
      " --------Done:\n",
      " 324.25123674860356\n",
      "--------\n",
      "15 Chapter 64 Scene Done: 146.51866856416189\n",
      "--------\n",
      "15 Chapter 65 Scene Done: 0\n",
      "--------\n",
      "15 Chapter 7 Scene Done: 136.98848323261717\n",
      "--------\n",
      "16 15Chapter  Chapter35  8Scene  SceneDone:  567.6856003651404Done: \n",
      "--------206.5367208682513\n",
      "\n",
      "--------\n",
      "15 Chapter 9 Scene Done: 0\n",
      "--------\n",
      "15 Chapter 10 Scene Done: 44.55948671090568\n",
      "--------\n",
      "15 Chapter 11 Scene Done: 0\n",
      "--------\n",
      "16 Chapter 89 Scene Done: 566.8204960921472\n",
      "--------\n",
      "15 Chapter 12 Scene Done: 47.56155168481396\n",
      "--------\n",
      "15 Chapter 106 Scene Done: 770.9244917532917\n",
      "--------\n",
      "16 Chapter 36 Scene Done: 385.673334642005\n",
      "--------\n",
      "15 Chapter 13 Scene Done: 432.902689181567\n",
      "--------\n",
      "16 Chapter 1637 Chapter Scene  90 Done:Scene 232.05739907026415 Done:\n",
      " --------435.54660688760845\n",
      "\n",
      "--------\n",
      "16 Chapter 91 Scene Done: 0\n",
      "--------\n",
      "15 Chapter 14 Scene Done: 623.1187403476497\n",
      "--------\n",
      "15 Chapter 107 Scene Done: 888.4274808556452\n",
      "--------\n",
      "15 Chapter 108 Scene Done: 590.0294937412456\n",
      "--------\n",
      "15 Chapter16  109Chapter  Scene 92Done:  Scene124.09287536273365 \n",
      "Done:-------- \n",
      "905.133273893622715\n",
      " --------Chapter\n",
      " 110 Scene Done: 0\n",
      "--------\n",
      "15 Chapter 111 Scene Done: 0\n",
      "--------\n",
      "15 Chapter 112 Scene Done: 290.96807048404975\n",
      "--------\n",
      "16 Chapter 93 Scene Done: 693.2659336004741\n",
      "--------\n",
      "15 Chapter 113 16Scene  Done:Chapter  759.58952591050338\n",
      " --------Scene\n",
      " Done: 1601.6180666966097\n",
      "--------\n",
      "1615  ChapterChapter  94114  SceneScene  Done:Done:  113.85189089465578265.3876277470012\n",
      "\n",
      "----------------\n",
      "\n",
      "16 Chapter 95 Scene Done: 521.5722460358514\n",
      "--------\n",
      "15 Chapter 115 Scene Done: 477.36485745560867\n",
      "--------\n",
      "16 Chapter 96 Scene Done: 157.448562254273\n",
      "--------\n",
      "15 Chapter 116 Scene Done: 373.98935570729907\n",
      "--------\n",
      "16 Chapter 39 Scene Done: 20.649590017555077\n",
      "--------\n",
      "16 Chapter 97 Scene Done: 567.336866712308\n",
      "--------\n",
      "16 Chapter 40 Scene Done: 159.44627787616176\n",
      "--------\n",
      "16 Chapter 98 Scene Done: 156.53531766034962\n",
      "--------\n",
      "16 Chapter 99 Scene Done: 166.39601017195827\n",
      "--------\n",
      "16 Chapter 100 Scene Done: 639.6033826850023\n",
      "--------\n",
      "16 Chapter 41 Scene Done: 678.5576733201611\n",
      "--------\n",
      "16 Chapter 42 Scene Done: 214.41652575642317\n",
      "--------\n",
      "16 Chapter 43 Scene Done: 46.259228291187156\n",
      "--------\n",
      "16 Chapter 44 Scene Done: 409.1094506564454\n",
      "--------\n",
      "16 Chapter 45 Scene Done: 595.1370617218004\n",
      "--------\n",
      "16 Chapter 46 Scene Done: 31.70993265248117\n",
      "--------\n",
      "16 Chapter 47 Scene Done: 449.20819916348484\n",
      "--------\n",
      "16 Chapter 48 Scene Done: 326.5041045205179\n",
      "--------\n",
      "16 Chapter 49 Scene Done: 144.2523259898598\n",
      "--------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process ForkPoolWorker-160:\n",
      "Process ForkPoolWorker-162:\n",
      "Process ForkPoolWorker-158:\n",
      "Process ForkPoolWorker-161:\n",
      "Process ForkPoolWorker-157:\n",
      "Process ForkPoolWorker-155:\n",
      "Process ForkPoolWorker-163:\n",
      "Process ForkPoolWorker-154:\n",
      "Process ForkPoolWorker-156:\n",
      "Process ForkPoolWorker-159:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/queues.py\", line 335, in get\n",
      "    res = self._reader.recv_bytes()\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/connection.py\", line 216, in recv_bytes\n",
      "    buf = self._recv_bytes(maxlength)\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/connection.py\", line 407, in _recv_bytes\n",
      "    buf = self._recv(4)\n",
      "  File \"/home/jd/anaconda3/envs/music/lib/python3.6/multiprocessing/connection.py\", line 379, in _recv\n",
      "    chunk = read(handle, remaining)\n",
      "KeyboardInterrupt\n"
     ]
    }
   ],
   "source": [
    "pool = Pool(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1285,
   "metadata": {},
   "outputs": [],
   "source": [
    "dialogueSimMat = np.zeros((CHAP_COUNT, SCENE_COUNT))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1286,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "\n",
    "STOPWORDS = stopwords.words('english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1287,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simMake(i, j):        \n",
    "    itf = 0\n",
    "    scores = []\n",
    "    dialogues = []\n",
    "    \n",
    "    for ii in range(len(chapterQuotes[i])):\n",
    "        for jj in range(len(videoDialogues[j])):\n",
    "\n",
    "            matched = lcs(chapterQuotes[i][ii], videoDialogues[j][jj])\n",
    "            matched = list(filter(lambda x: x not in STOPWORDS, matched))\n",
    "            \n",
    "            localItf = 0\n",
    "            \n",
    "            for word in matched:\n",
    "                if word in videoWordCount.keys():\n",
    "                    localItf += np.log(1/videoWordCount[word])\n",
    "                else:\n",
    "                    print(word, \"missing\")\n",
    "                    \n",
    "            scores.append(localItf * len(matched))\n",
    "            dialogues.append(matched)\n",
    "            \n",
    "            itf += localItf * len(matched)\n",
    "    \n",
    "    dialogueSimMat[i, j] = itf\n",
    "    \n",
    "    print(i, \"Chapter\", j, \"Scene\", \"Done:\", -itf)    \n",
    "    print(\"--------\")\n",
    "    \n",
    "    return itf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1288,
   "metadata": {},
   "outputs": [],
   "source": [
    "iterables = []\n",
    "\n",
    "for i in range(CHAP_COUNT):\n",
    "    for j in range(SCENE_COUNT):\n",
    "        iterables.append((i, j))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1289,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "result = pool.starmap(simMake, iterables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1290,
   "metadata": {},
   "outputs": [],
   "source": [
    "for idx, iterable in enumerate(iterables):\n",
    "    i, j = iterable\n",
    "    dialogueSimMat[i][j] = result[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 7200x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(-dialogueSimMatLoad/np.max(-dialogueSimMatLoad))\n",
    "plt.colorbar()\n",
    "plt.gcf().set_figwidth(100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normed Similarity Matrix for Dialogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1509,
   "metadata": {},
   "outputs": [],
   "source": [
    "normedDialogueSimMat = -dialogueSimMat/np.max(-dialogueSimMat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1510,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 1510,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "savecache(dialogueSimMat, 'dialogueMatch_120')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1511,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 1511,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "savecache(normedDialogueSimMat, 'normedDialogueSimMat_120')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "dialogueSimMatLoad = loadcache('dialogueMatch_120')\n",
    "normedDialogueSimMat = loadcache('normedDialogueSimMat_120')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "import igraph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = igraph.Graph(directed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "NC = CHAP_COUNT\n",
    "NS = SCENE_COUNT\n",
    "S_vertex = NC * NS\n",
    "T_vertex = NC * NS + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Since igraph only supports integer indexing, we convert pixel coordinates to integer.\n",
    "\"\"\"\n",
    "def tuple_to_int(i, j, col):\n",
    "    return i * col + j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "This converts hashed nodes back to the pixel coordinates.\n",
    "\"\"\"\n",
    "def int_to_tuple(pt, col):\n",
    "    y = pt % col\n",
    "    x = (pt - y)/col\n",
    "    return (x, y)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make Vertices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 2007)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vertices = [S_vertex, T_vertex]\n",
    "_ = [[vertices.append(tuple_to_int(i, j, NS)) for j in range(NS)] for i in range(NC)]\n",
    "min(vertices), max(vertices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make Edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "edges = []\n",
    "capacity = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha = 1\n",
    "gs = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add edges from source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(NC):\n",
    "    gaussian_factor = 2 - np.exp(-(i) ** 2/(2*NS**2)) # Equation 7 (Global)\n",
    "    \n",
    "#     print(gaussian_factor, S_vertex, tuple_to_int(i, 0, NS))\n",
    "    \n",
    "    edges.append([S_vertex, tuple_to_int(i, 0, NS)])\n",
    "    capacity.append(gaussian_factor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add edges to sink"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "for i in range(NC):\n",
    "    gaussian_factor = 2 - np.exp(-(NC-i-1) ** 2/(2*NS**2)) # Equation 7 (Global)  \n",
    "    \n",
    "#     print(gaussian_factor, tuple_to_int(i, NS-1, NS), T_vertex)\n",
    "    \n",
    "    if i == NC-1:\n",
    "        gaussian_factor = 0\n",
    "    \n",
    "    edges.append([tuple_to_int(i, NS-1, NS), T_vertex])\n",
    "    capacity.append(gaussian_factor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0091506364544904)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(capacity), np.max(capacity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add edge from scene s to scene s+1 for all chapters - local distance - Includes global likelihood\n",
    "\n",
    "Also subtract the similarity values based on character histograms and "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**This is not done for the first row - no chapter**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "edgeWeightMat = np.zeros_like(normCharacterSimMat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "for j in range(NS-1): # Cannot do the last column / scene\n",
    "    for i in range(NC):  # For every chapter\n",
    "        for k in range(NC): # For every next chapter             \n",
    "            \n",
    "            if k < i or k > i+1: # FORCE all chapters to only jump to next, not further\n",
    "                continue\n",
    "            \n",
    "            weight = alpha + ((k-i)**2/(2 * NC**2)) # Equation 6 (Local)\n",
    "            gaussian_factor = 2 - np.exp(-(j - i*(NS/NC)) ** 2/(2*NS**2)) # Equation 7 (Global)\n",
    "                        \n",
    "            weight *= gaussian_factor\n",
    "            gs.append(gaussian_factor)\n",
    "                        \n",
    "            weight -= 0.5 * normCharacterSimMat[k, j+1]\n",
    "            weight -= 0.5 * normedDialogueSimMat[k, j+1]\n",
    "\n",
    "#             print(weight, i, j, \"->\", k, j+1)\n",
    "                        \n",
    "            if weight < 0:\n",
    "                print(weight)\n",
    "                weight = 0\n",
    "\n",
    "#             weight *= gaussian_factor\n",
    "                        \n",
    "            edgeWeightMat[k, j+1] = weight\n",
    "            edges.append([tuple_to_int(i, j, NS), tuple_to_int(k, j+1, NS)])\n",
    "            capacity.append(weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(capacity)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 7200x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(edgeWeightMat)\n",
    "plt.colorbar()\n",
    "plt.gcf().set_figwidth(100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 7200x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(0.5 * normCharacterSimMat + 0.5 * normedDialogueSimMat)\n",
    "plt.colorbar()\n",
    "plt.gcf().set_figwidth(100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu_g = np.mean(gs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add Things to the Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "G.add_vertices(vertices)\n",
    "G.add_edges(edges, {'capacity': capacity})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compute Shortest Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "\n",
    "matplotlib.use(\"pgf\")\n",
    "matplotlib.rcParams.update({\n",
    "    \"pgf.texsystem\": \"pdflatex\",\n",
    "    'font.family': 'serif',\n",
    "    'text.usetex': True,\n",
    "    'pgf.rcfonts': False,\n",
    "    'font.size': 15,\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path = G.get_shortest_paths(S_vertex, to=T_vertex, weights=capacity, mode=\"out\", output='vpath')[0][1:-1]\n",
    "alignment = []\n",
    "\n",
    "plt.imshow(edgeWeightMat, cmap='coolwarm_r')\n",
    "prevI = 0\n",
    "prevJ = 0\n",
    "\n",
    "for node in path:\n",
    "    chap, scene = int_to_tuple(node, NS)\n",
    "    \n",
    "    alignment.append({\n",
    "        'chapter': chap,\n",
    "        'scene': scene\n",
    "    })\n",
    "    \n",
    "    plt.plot([prevJ, scene], [prevI, chap], color=\"red\")\n",
    "    \n",
    "    prevI = chap\n",
    "    prevJ = scene\n",
    "    \n",
    "    \n",
    "plt.colorbar()\n",
    "plt.gca().set_ylabel(\"Chapter Number\")\n",
    "plt.gca().set_xlabel(\"Scene Number\")\n",
    "plt.gcf().set_figwidth(30)\n",
    "# plt.savefig(\"graph_b2m.pdf\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save to cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "savecache(alignment, 'alignment')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}